Evaluating SEDCoT on a public COBOL-to-C dataset demonstrates that it outperforms state-of-the-art baselines by at least 12% while producing translations with substantially higher readability than rule-based alternatives.
Abstract
COBOL remains critical across banking, insurance, and government infrastructure. However, maintenance is increasingly challenging due to outdated technologies, sparse documentation, and developer retirement, necessitating code translation into modern languages like C. Traditional rule-based transcompilers yield outputs that are difficult to read and maintain, while general-purpose large language models (LLMs) achieve suboptimal correctness because COBOL is a low-resource language with distinct logic patterns. To bridge this gap, we propose SEDCoT, a novel COBOL-to-C translation framework. SEDCoT first leverages LLMs for initial translation, then combines symbolic execution with LLM guidance to generate test suites and iteratively repair semantic discrepancies. Finally, it integrates delta debugging to minimize failing tests into succinct counterexamples, accelerating automated code repair. Evaluating SEDCoT on a public COBOL-to-C dataset demonstrates that it outperforms state-of-the-art baselines by at least 12% while producing translations with substantially higher readability than rule-based alternatives.
Decompilation aims to recover high-level, compilable, and semantically equivalent code from binaries. Traditional decompilers produce pseudo-C that is difficult to read and does not compile, while the recent LLM-assisted approaches generate readable, but semantically incorrect code. LLM-aided iterative recovery is an emerging branch of research, but prior works rely on supplied test suites for semantic recovery. In this work, we present CHISEL, a test suite-free framework to iteratively recover source code from Ghidra-derived pseudo-C. CHISEL uses simple yet effective feedback from a compiler (static analysis) and a coverage-guided fuzzer (differential analysis), augmented by rich observables for grounded divergence detection and feedback, cross-iteration divergence memory, and best candidate retention. We systematically evaluate CHISEL for compilation and semantic recovery, feedback oracle soundness, and iteration overhead on 120 ExeBench functions compiled for the x86-64 architecture, across four optimizations (O0-O3), in both stripped and unstripped variants, using the open-weight Gemma4:31b LLM. CHISEL, with all recommended features, achieves an average of 96.1% re-compilability and 79.8% re-executability rates at an average of 2.1 iterations. Significantly, CHISEL recovers 26% of first-generation execution errors. At the same time, CHISEL feedback oracle falsely accepts only 9.4% candidates. Lastly, CHISEL performs significantly better than two recent prior work on LLM-assisted decompilation.
Varun Kohli, N. Raghava, B. Sikdar et al.· 0 citations
The results indicate that functional correctness in code generation can be meaningfully improved without modifying the backbone architecture, by jointly optimizing how tasks are prompted, how the model is adapted, and how final outputs are selected.
This work introduces a search-based approach that identifies and evolves a set of natural language transformation rules with strong downstream effects on coding performance, and proposes DUALFIX, a staged repair pipeline that combines the evolved transformation rules with execution-feedback repair, addressing both specification-level and implementation-level failures.
Amal Akli, Melissa Akli, Cedric Richter et al.· 0 citations
Large language models (LLMs) are increasingly used to port scientific codes across heterogeneous high-performance computing (HPC) programming models, such as translating CUDA to OpenMP, OpenACC, Kokkos or SYCL. However, current evaluations use compilation success, token-level similarity, or developer-written tests from static benchmarks, which cannot reliably ensure behavioral correctness. We present Kaizen, a metamorphic fuzzing and differential testing framework for evaluating the correctness of LLM-translated HPC code. Kaizen uses metamorphic fuzzing via source-code mutation to generate semantically equivalent programs, grammar-based input fuzzing to explore behavioral diversity, and differential testing to expose semantic divergences between original and translated applications that compile and pass developer-written tests yet produce incorrect scientific results. We evaluate Kaizen on CUDA-to-OpenMP translation of 16 scientific applications from seven domains using three fine-tuned LLMs at kernel-level and full-program granularity. Our evaluation reveals that (1) compilation success is a poor proxy for correctness; (2) LLM-translated programs exhibit systematic compile-time error patterns, with nine categories for kernel-level translation and 27 for full-program translation; (3) semantic errors that survive compilation are often input-dependent and require differential testing to expose; and (4) full-program translation is substantially harder than kernel-level translation. These findings highlight the need for correctness-oriented evaluation of LLM-assisted HPC code translations.
Oscar Ludwig, Ninad Anklesaria, Zheming Jin et al.· 0 citations
This work proposes VPID, a multi-agent framework for generating complex Verilog that achieves monotonic functional improvement and introduces an experience-guided refinement strategy that distills historical waveform mismatches into constraints, guiding the targeted debugging for the unverified ports.
Hongguang Wang, Jiaming Guo, Rui Zhang et al.· 0 citations
This paper identifies patch verbosity as a major yet overlooked concern in LLM-based APR and proposes RECAP, a lightweight, plug-and-play adapter that attaches to existing repair frameworks after generation that achieves a substantially better size-correctness tradeoff.
Wenqiang Luo, J. Keung, Xiaoyu Shi et al.· 0 citations